The new tendencies in designing real-time systems indicate that the future applications will be built on reconfigurable hardware devices. These applications require high performance and reasonable flexibility towards user and environment needs. To fulfill these application requirements, the density of heterogeneous resources evolves within these devices. Hence, the complexity of these devices leads to the search of efficient mechanisms to manage hardware resources. The proposed placement methods suffer from issues of fragmentation, tasks rejection, and overheads. This paper focuses on an off-line flow of hardware tasks' classification that aims at the optimized use of the resources and targets all above mentioned issues.
Currently, reconfigurable hardware devices feature a high density of heterogeneous resources to enable multitasking and offer flexibility in application needs. These concepts raise the need for efficient management of hardware tasks and hardware resources. The scheduling of hardware tasks is highly dependent on placement. Placement focuses on allocation of hardware resources required by the scheduled hardware tasks. In this paper, we propose novel three-level resource management that investigates enhancement of placement quality by reducing task rejection, configuration overheads, and by optimizing resource utilization. Improving placement quality will produce significant enhancement of performance for scheduling and overall execution time of the application in FPGA. Hence, the placement problem is formulated into a constrained optimization problem and resolved with powerful solvers using the Branch and Bound method. The obtained results of an application of heterogeneous hardware tasks show an average resource utilization of 36% of the available resources on the reconfigurable region and an overall overhead of 11% of total application running time, and we have eliminated the issue of task rejection. Compared to static implementation, the gain in resource utilization within the reconfigurable region achieves up to 43%.
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